Update app.py

app.py

@@ -3,11 +3,8 @@ import pandas as pd
 import openai
 import joblib
 from PIL import Image
-import requests
-from io import BytesIO
 import matplotlib.pyplot as plt
 import numpy as np
-from sklearn.preprocessing import LabelEncoder
 from huggingface_hub import hf_hub_download
 from transformers import AutoFeatureExtractor, AutoModelForImageClassification
 import torch
@@ -24,9 +21,6 @@ def load_datasets():
         st.error(f"Error loading dataset: {str(e)}")
         raise e
 
-def load_image(image_file):
-    return Image.open(image_file)
-
 def classify_image(image):
     try:
         # Load the model and feature extractor
@@ -114,6 +108,10 @@ model = load_model_and_encodings()
 # Initialize OpenAI API key
 openai.api_key = st.secrets["GPT_TOKEN"]
 
+# Get the session state
+if 'image' not in st.session_state:
+    st.session_state.image = None
+
 # File uploader for image
 uploaded_file = st.file_uploader("Choose a car image", type=["jpg", "jpeg", "png"])
 
@@ -121,40 +119,35 @@ uploaded_file = st.file_uploader("Choose a car image", type=["jpg", "jpeg", "png
 camera_image = st.camera_input("Or take a picture of the car")
 
 # Process the image (either uploaded or from camera)
-image = None
-st.write(f"uploaded_file: {uploaded_file}")
-st.write(f"camera_image: {camera_image}")
-
 if uploaded_file is not None:
     st.write("Attempting to open uploaded file...")
     try:
-        image = Image.open(uploaded_file)
+        st.session_state.image = Image.open(uploaded_file)
         st.write("Image uploaded successfully.")
     except Exception as e:
         st.error(f"Error opening uploaded file: {str(e)}")
 elif camera_image is not None:
     st.write("Attempting to open camera image...")
     try:
-        image = Image.open(camera_image)
+        st.session_state.image = Image.open(camera_image)
         st.write("Image captured successfully.")
     except Exception as e:
         st.error(f"Error opening camera image: {str(e)}")
 
-st.write(f"image: {image}")
-
-if image is not None:
-    st.image(image, caption='Processed Image', use_container_width=True)
+# Display the processed image
+if st.session_state.image is not None:
+    st.image(st.session_state.image, caption='Processed Image', use_container_width=True)
 
     # Classify the car image
     with st.spinner('Analyzing image...'):
-        car_classifications = classify_image(image)
+        car_classifications = classify_image(st.session_state.image)
     
     if car_classifications:
         st.write("Image classification successful.")
         st.subheader("Car Classification Results:")
         for classification in car_classifications:
             st.write(f"Model: {classification['label']}")
-            st.write(f"Confidence: {classification['score']*100:.2f}%")
+            st.write(f"Confidence: {classification['score'] * 100:.2f}%")
         
         # Use the top prediction for further processing
         top_prediction = car_classifications[0]['label']